Fast multi-task learning for query spelling correction
Proceedings of the 21st ACM international conference on Information and knowledge management
Wearable computing: accelerometers' data classification of body postures and movements
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
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Personalized activity recognition usually faces the problem of data sparseness. We aim at improving accuracy of personalized activity recognition by incorporating the information from other persons. We propose a new online multi-task learning method for personalized activity recognition. The proposed online multi-task learning method automatically learns the ``transfer-factors" (similarities) among different tasks (i.e., among different persons in our case). Experiments demonstrate that the proposed method significantly outperforms existing methods. The novelty of this paper is twofold: (1) A new multi-task learning framework, which can naturally learn similarities among tasks, (2) To our knowledge, this is the first study of large-scale personalized activity recognition.